Campuses:

Assessing Internal Climate Variability with Few Ensemble Runs

Tuesday, April 24, 2018 - 9:00am - 9:30am
Keller 3-180
While large climate model ensembles provide many insights, they also present a large burden in terms of computational resources and storage requirements. A complementary approach to large ensembles is to train statistical models on fewer runs. While far from capturing the complexity and high variable-dimensionality of climate model runs, simulations from a simpler statistical model might nevertheless provide insights on scientific questions of interest such as the variability of regional trends. We demonstrate an example by comparing runs from a large ensemble project and a statistical model trained with four of the large ensemble runs and show that the variability of regional temperature trends is indistinguishable between the two set of runs. Training statistical models on fewer runs might prove especially useful in the context of large climate model inter-comparison projects where creating large ensembles for each model is challenging and statistical models can be employed to gain further insights regarding internal variability.